Optimizing Industrial Engineering Performance with Fuzzy CNN Framework for Efficiency and Productivity

In industrial engineering, efficiency is paramount. Convolutional Neural Networks (CNNs) are commonly used to identify and detect labour activity in industrial environments. Accurate fault detection is crucial for identifying and classifying defects in production. This research proposes a novel approach to enhancing industrial performance by predicting defects in manufacturing processes using a fuzzy-based CNN technique. The framework integrates cutting-edge fuzzy logic with CNNs, improving diagnostic model efficacy through fuzzy logic-based weight adjustments during training. Additionally, a novel fuzzy classification method is used for defect detection, followed by a demand forecast error simulation tailored to specific regions. The framework begins with initial training data, which is then combined with multiple classifiers to form a comprehensive dataset. The CNN, enhanced by fuzzy logic for weight updates, first employs fuzzy classification to diagnose errors, then simulates demand forecast errors regionally. This refined dataset is subsequently used as input for the CNN. Implementation in a manufacturing organization demonstrates the proposed framework's effectiveness, significantly improving fault diagnostic accuracy compared to traditional methods. By leveraging the latest advances in CNNs and fuzzy logic, the framework offers a robust solution for boosting industrial efficiency. This comprehensive approach to defect detection in industrial processes seamlessly integrates CNNs with fuzzy logic, highlighting the framework's utility and potential impact on industrial efficiency. The results underscore the viability of this innovative technology in enhancing industrial engineering performance.

@article{Bandhekar2024,
  title     = {Optimizing Industrial Engineering Performance with Fuzzy CNN Framework for Efficiency and Productivity},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Suraj Bandhekar and Abdul Hameed Kalifullah and Venkata Krishna Rao Likki and Hatem S. A. Hamatta and Deepa and Tumikipalli Nagaraju Yadav},
  doi       = {10.14569/IJACSA.2024.01506126},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506126}
}